abcd

chains/openai_model.py

@@ -6,7 +6,7 @@ from langchain.prompts import PromptTemplate
 from config import TIMEOUT_STREAM
 from vector_db import upload_file
 from callback import StreamingGradioCallbackHandler
-from queue import SimpleQueue, Empty
+from queue import SimpleQueue, Empty, Queue
 from threading import Thread
 from utils import history_file_path, load_lasted_file_username, add_source_numbers, add_details
 from chains.custom_chain import CustomConversationalRetrievalChain
@@ -197,25 +197,24 @@ class OpenAIModel:
             status_text = "Request URL: " + OPENAI_API_BASE
             yield chatbot, status_text
             # Create a funciton to call - this will run in a thread
-
             # Create a Queue object
             response_queue = SimpleQueue()
+
             def task():
                 # Converation + RetrivalChain
                 qa = CustomConversationalRetrievalChain.from_llm(llm, vectorstore.as_retriever(k=5),
-                                                condense_question_llm = condense_llm, verbose=True, 
+                                                condense_question_llm = condense_llm, verbose=True,
                                                 condense_question_prompt=condense_prompt,
                                                 combine_docs_chain_kwargs={"prompt": qa_prompt},
                                                 return_source_documents=True)
                 # query with input and chat history
                 response = qa({"question": inputs, "chat_history": self.history})
-                # Put response in the queue
                 response_queue.put(response)
                 q.put(job_done)
             
             
             thread = Thread(target=task)
-            thread.start()   
+            thread.start()
             chatbot.append((inputs, ""))
             content = ""
             while True:
@@ -228,9 +227,8 @@ class OpenAIModel:
                     yield chatbot, status_text
                 except Empty:
                     continue
-            
+
             # add citation info to response
-            # Get the response from the queue
             response = response_queue.get()
             relevant_docs = response["source_documents"]
             reference_results = [d.page_content for d in relevant_docs]

requirements.txt

@@ -6,4 +6,5 @@ gradio_client==0.2.7
 tiktoken
 pinecone-client
 google-api-python-client
+bs4
 facebook-page-scraper